Dana H Ballard
- E-mail: firstname.lastname@example.org
- Phone: (512) 471-9750
- Office: CSA 1.138
My main research interest is in computational theories of the brain with emphasis on human vision. In 1985 Chris Brown and I led a team that designed and built a high speed binocular camera control system capable of simulating human eye movements. The system was mounted on a robotic arm that allowed it to move at one meter per second in a two meter radius workspace. This system has led to an increased understanding of the role of behavior in vision. The theoretical aspects of that system were summarized in a paper ``Animate Vision,'' which received the Best Paper Award at the 1989 International Joint Conference on Artificial Intelligence. Currently I am interested in pursuing this research by using model humans in virtual reality environments. In addtion I am interested in models of the brain that relate to detailed neural codes. A position paper on this work appeared in the Behavioral and Brain Sciences.
Hayhoe, M. & Ballard, D. (2009, September) Modeling the role of task in the control of gaze. Visual Cognition, 17(6-7), pp. 1185-1204.
Hayhoe M. & Ballard, D. (2005) Eye movements in natural behavior. Trends in Cognitive Sciences, 9(4), 188-193.
Sprague, N. and Ballard, D.H. (2005) Modeling embodied visual behaviors. ACM Transactions on Applied Perception. (Submitted)
Yu, Chen and Dana H. Ballard,(2004), A Multimodal Learning Interface for Grounding Spoken Language in Sensorimotor Experience, ACM Transactions on Applied Perception. 1, 1.
Triesch, J., Ballard, D., Hayhoe, M., & Sullivan, B. (2003). What you see is what you need. Journal of Vision, 3, 86-94.
Shimozaki, S., Zelinsky, G., Hayhoe, H., Merigan, W., & Ballard, D. (2003). Spatial memory and saccade targeting deficits from parietal injury. Neuropsychologia, 41, pp. 1365-1386.
Rao, R., Zelinsky, G., Hayhoe, M., & Ballard, D. (2002). Eye movements in iconic visual search. Vision Research, 42(11), 1447-1463.
Triesch, J., Ballard, D.H., and Jacobs, R.A. (2002) Fast temporal dynamics of visual cue integration. Perception, 31, 421-434
Triesch, J., Sullivan, B., Hayhoe, M., & Ballard, D. (2002). Saccade contingent scene changes in unconstrained virtual reality. Proceedings, Eye Tracking Research & Application, 95-102.
Zuohua Zhang, Dana H. Ballard,(2001) Distributed Synchrony, Journal of Neurocomputing, Vol 44-46C, 715-720.
Rao, R.P.N. and D.H. Ballard,(1999), Predictive coding in the visual cortex: A functional interpretation of some extra-classical receptive-field effects, Nature Neuroscience 2, 1, 79.
de Sa, V.R., & Ballard, D. (1998) Category Learning through Multi-Modality Sensing, Neural Computation 10(5).
Hayhoe, M., Bensinger, D., & Ballard, D. (1998). Task constraints in visual working memory. Vision Research, 38, 125-137.
Ballard, D., Hayhoe, M., Pook, P., & Rao, R. (1997). Deictic codes for the embodiment of cognition. Behavioral and Brain Sciences, 20, 723-767.
Zelinsky, G., Rao, R., Hayhoe, M., & Ballard, D. (1997). Eye movements reveal the spatio-temporal dynamics of visual search. Psychological Science, 8, 448-453.
Rao, R., Zelinsky, G., Hayhoe, M., & Ballard, D. (1996). Modelling saccade targeting in visual search. In D. Touretzky, M. Mozer, & M. Hasselmo (Eds). Advances in Neural Information Processing Systems, 8, pp. 830-836. Cambridge, MA: MIT Press.
Smeets, J., Hayhoe, M., & Ballard, D. (1996). Influence of hand movements on eye-head coordination. Experimental Brain Research, 109, 434-440.
Ballard, D., Hayhoe, M., & Pelz, J. (1995). Memory representations in natural tasks. Cognitive Neuroscience, 7, 66-80.
Ballard, D.H., "Animate vision," Artificial Intelligence Journal 48, 57-86, 1991.
Swain, M. J. & Ballard, D. H. (1991) Color Indexing, International Journal of Computer Vision, 7,1,11-32.
Ballard, D.H., G.E. Hinton, and T.J. Sejnowski (1983), Parallel visual computation, Nature 306, 5938, 21-26, 3.
Ballard, D. H. (1981), Strip trees: a hierarchical representation for curves, Communications of the ACM, v.24 n.5, 310-321.
Ballard, D.H., "Generalizing the Hough transform to detect arbitrary shapes, " Pattern Recognition 13, 2, April 1981.
C S 391L MACHINE LEARNING